Electrochemical ohmic memristors for continual learning
Abstract Developing versatile and reliable memristive devices is crucial for advancing future memory and computing architectures. The years of intensive research have still not reached and demonstrated their full horizon of capabilities, and new concepts are essential for successfully using the comp...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-03-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-025-57543-w |
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| _version_ | 1850251517547773952 |
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| author | Shaochuan Chen Zhen Yang Heinrich Hartmann Astrid Besmehn Yuchao Yang Ilia Valov |
| author_facet | Shaochuan Chen Zhen Yang Heinrich Hartmann Astrid Besmehn Yuchao Yang Ilia Valov |
| author_sort | Shaochuan Chen |
| collection | DOAJ |
| description | Abstract Developing versatile and reliable memristive devices is crucial for advancing future memory and computing architectures. The years of intensive research have still not reached and demonstrated their full horizon of capabilities, and new concepts are essential for successfully using the complete spectra of memristive functionalities for industrial applications. Here, we introduce two-terminal ohmic memristor, characterized by a different type of switching defined as filament conductivity change mechanism (FCM). The operation is based entirely on localized electrochemical redox reactions, resulting in essential advantages such as ultra-stable binary and analog switching, broad voltage stability window, high temperature stability, high switching ratio and good endurance. The multifunctional properties enabled by the FCM can be effectively used to overcome the catastrophic forgetting problem in conventional deep neural networks. Our findings represent an important milestone in resistive switching fundamentals and provide an effective approach for designing memristive system, expanding the horizon of functionalities and neuroscience applications. |
| format | Article |
| id | doaj-art-8e5234b352b140bda20d3ded15bd5052 |
| institution | OA Journals |
| issn | 2041-1723 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-8e5234b352b140bda20d3ded15bd50522025-08-20T01:57:52ZengNature PortfolioNature Communications2041-17232025-03-0116111310.1038/s41467-025-57543-wElectrochemical ohmic memristors for continual learningShaochuan Chen0Zhen Yang1Heinrich Hartmann2Astrid Besmehn3Yuchao Yang4Ilia Valov5Institute of Materials in Electrical Engineering 2 (IWE2), RWTH Aachen UniversityBeijing Advanced Innovation Center for Integrated Circuits, School of Integrated Circuits, Peking UniversityCentral Institute for Engineering, Electronics and Analytics (ZEA-3), Forschungszentrum JülichCentral Institute for Engineering, Electronics and Analytics (ZEA-3), Forschungszentrum JülichBeijing Advanced Innovation Center for Integrated Circuits, School of Integrated Circuits, Peking UniversityPeter Grünberg Institute 7 and JARA-FIT, Forschungszentrum JülichAbstract Developing versatile and reliable memristive devices is crucial for advancing future memory and computing architectures. The years of intensive research have still not reached and demonstrated their full horizon of capabilities, and new concepts are essential for successfully using the complete spectra of memristive functionalities for industrial applications. Here, we introduce two-terminal ohmic memristor, characterized by a different type of switching defined as filament conductivity change mechanism (FCM). The operation is based entirely on localized electrochemical redox reactions, resulting in essential advantages such as ultra-stable binary and analog switching, broad voltage stability window, high temperature stability, high switching ratio and good endurance. The multifunctional properties enabled by the FCM can be effectively used to overcome the catastrophic forgetting problem in conventional deep neural networks. Our findings represent an important milestone in resistive switching fundamentals and provide an effective approach for designing memristive system, expanding the horizon of functionalities and neuroscience applications.https://doi.org/10.1038/s41467-025-57543-w |
| spellingShingle | Shaochuan Chen Zhen Yang Heinrich Hartmann Astrid Besmehn Yuchao Yang Ilia Valov Electrochemical ohmic memristors for continual learning Nature Communications |
| title | Electrochemical ohmic memristors for continual learning |
| title_full | Electrochemical ohmic memristors for continual learning |
| title_fullStr | Electrochemical ohmic memristors for continual learning |
| title_full_unstemmed | Electrochemical ohmic memristors for continual learning |
| title_short | Electrochemical ohmic memristors for continual learning |
| title_sort | electrochemical ohmic memristors for continual learning |
| url | https://doi.org/10.1038/s41467-025-57543-w |
| work_keys_str_mv | AT shaochuanchen electrochemicalohmicmemristorsforcontinuallearning AT zhenyang electrochemicalohmicmemristorsforcontinuallearning AT heinrichhartmann electrochemicalohmicmemristorsforcontinuallearning AT astridbesmehn electrochemicalohmicmemristorsforcontinuallearning AT yuchaoyang electrochemicalohmicmemristorsforcontinuallearning AT iliavalov electrochemicalohmicmemristorsforcontinuallearning |